Ultrasonic diagnosis apparatus and program

ABSTRACT

According to one embodiment, an ultrasonic diagnosis apparatus is configured as follows. Namely, the apparatus includes a first unit configured to generate a first transmission wave for acquisition of a first image with a higher priority on sensitivity than on resolution, a second unit configured to generate a second transmission wave for acquisition of a second image with a higher priority on resolution than on sensitivity, an analysis unit configured to perform multiresolution analysis based on predetermined transform processing on the first image and the second image, a filter unit configured to perform predetermined filter operation for corresponding coefficients of the first image and the second image for each coefficient of each resolution acquired by the analysis unit, and an inverse transform unit configured to generate a composite image of the first image and the second image by performing inverse transform processing of transform processing by the analysis unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2009-187819, filed Aug. 13, 2009; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an ultrasonic diagnosisapparatus and a program for causing a computer to function as theultrasonic diagnosis apparatus and, more particularly, to an ultrasonicdiagnosis apparatus which acquires and displays a tissue image or acontrast-enhanced bubble image by transmitting and receiving ultrasonicwaves and a program which causes a computer to function as theultrasonic diagnosis apparatus.

BACKGROUND

Recently, ultrasonic diagnosis apparatuses have been widely used in thegeneral medical field. An ultrasonic diagnosis apparatus allowsreal-time visual recognition of, for example, the pulsation of the heartof an object and the movement of a fetus with the simple operation ofonly bringing an ultrasonic probe into contact with the body surface ofthe object. The ultrasonic diagnosis apparatus is considered highly safefor human bodies, and can be repeatedly used for examination.

In addition, the ultrasonic diagnosis apparatus is smaller in systemsize than other medical image diagnosis apparatuses such as an X-raydiagnosis apparatus, CT apparatus, and MRI apparatus. Therefore, forexample, this apparatus allows easy examination upon being moved to abed side. More specifically, although it depends on the types offunctions of the ultrasonic diagnosis apparatuses, for example, compactapparatuses which can be carried with, for example, one hand have beendeveloped.

As described above, the ultrasonic diagnosis apparatus is free from theinfluence of exposure to radiation and the like and is small in size,and hence can be used for home medical care services and the like.

The ultrasonic diagnosis apparatus has a problem of so-called specklenoise caused by simultaneous occurrence of reflection and scattering ofultrasonic waves due to a medium, small bioligical tissue, or the likein an object. This speckle noise degrades not only the image quality ofa video but also accuracy in the display of an important form such asthe boundary between a body organ to be observed and the background.Such speckle noise is a significant trouble in fields of videointerpretation, organ recognition, and the like using videos acquired bythe ultrasonic diagnosis apparatus.

In order to solve this problem, for example, the following technique isdisclosed in Jpn. Pat. Appln. KOKAI Publication No. 2006-116307.

That is, Jpn. Pat. Appln. KOKAI Publication No. 2006-116307 discloses atechnique including a step (a) of decomposing a two-dimensionalultrasonic input video into a plurality of multiresolution videos at N(a positive integer) levels, a step (b) of determining thecharacteristic of each pixel of each decomposed video, a step (c) ofexecuting image quality improvement processing for each decomposed videobased on the pixel characteristics, a step (d) of executing 1-levelcomposition of the decomposed videos, and a step (e) of repeatedlyexecuting the steps (b) to (d) until the size of the composite videobecomes equal to that of the above two-dimensional ultrasonic video.More specifically, wavelet transform is used for multiresolutionanalysis used in the step (a).

The technique disclosed in Jpn. Pat. Appln. KOKAI Publication No.2006-116307 removes speckle noise and hence improves the image qualityof a video acquired by the ultrasonic diagnosis apparatus.

The ultrasonic diagnosis apparatus performs imaging by transmittingultrasonic waves from the ultrasonic probe to the inside of an objectand receiving reflected signals from the inside of the object. For thisreason, in the process of propagation of ultrasonic waves in an object,for example, ultrasonic waves are scattered or attenuated. That is, thedeeper a region is located in an object, the more difficult to visualizeit by the ultrasonic diagnosis apparatus.

More specifically, the distance resolution is superior but the depthsensitivity is inferior when the wave train length of the transmissionwaveform of an ultrasonic wave is short than when the wave train lengthis long. In addition, the higher the frequency of an ultrasonic wave,the higher the spatial resolution. However, since the degree ofattenuation of the ultrasonic wave during propagation increases, thedepth sensitivity decreases.

The same applies to contrast-enhanced ultrasonography. Incontrast-enhanced ultrasonography, the detection sensitivity ofcontrast-enhanced bubbles is obviously important. The ability tovisually recognize contrast-enhanced bubbles with high spatialresolution is also important in a clinical point of view because, forexample, it facilitates visual recognition of the marginal informationof a lesion.

As described above, in the ultrasonic diagnosis apparatus, there existsthe so-called tradeoff between the sensitivity of an acquired video andthe resolution, and it is very difficult to satisfy both therequirements.

Note that the technique disclosed in Jpn. Pat. Appln. KOKAI PublicationNo. 2006-116307 is not a technique that solves this problem.

The present invention has been made in consideration of the abovesituation, and has as its object to provide an ultrasonic diagnosisapparatus and program which can acquire a video that satisfies both therequirements for sensitivity (luminance) and resolution (visibility).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the system configuration of anultrasonic diagnosis apparatus according to an embodiment of the presentinvention;

FIG. 2 is a view showing the principle of image composition processingby an image generating circuit 24 in an ultrasonic diagnosis apparatus10 according to this embodiment;

FIG. 3 is a view for explaining the processing of acquiring images a andb by switching transmission waves alternately for each frame, andgenerating a composite image c by performing the above image compositionprocessing between two consecutive frames;

FIG. 4 is a view for explaining the processing of acquiring the images aand b by switching transmission waves for each raster, and generating acomposite image c by performing image composition processing for eachraster;

FIG. 5 is a block diagram showing an example of the system configurationof an ultrasonic diagnosis apparatus according to a modification;

FIG. 6 is a view showing a processing (speckle removal processing)procedure based on a speckle removal function executed by a speckleremoval processing unit; and

FIG. 7 is a flowchart showing a filter processing procedure executed bya nonlinear anisotropic diffusion filter.

DETAILED DESCRIPTION

In general, according to one embodiment, an ultrasonic diagnosisapparatus which acquires tomographic image data by scanning apredetermined region of an object with an ultrasonic wave has thefollowing arrangement. That is, the ultrasonic diagnosis apparatusincludes an ultrasonic probe configured to transmit an ultrasonic waveto the object and receive an echo signal from inside the object, a firsttransmission wave generating unit configured to generate, as atransmission wave for the scanning, a first transmission wave foracquisition of an image with a higher priority on sensitivity than onresolution, a second transmission wave generating unit configured togenerate, as a transmission wave for the scanning, a second transmissionwave for acquisition of an image with a higher priority on resolutionthan on sensitivity, a multiresolution analysis unit configured toperform multiresolution analysis based on predetermined transformprocessing on the first image acquired by scanning using the firsttransmission wave and the second image acquired by scanning using thesecond transmission wave, a filter operation unit configured to performpredetermined filter operation for corresponding coefficients of thefirst image and the second image for each coefficient of each resolutionacquired by the multiresolution analysis unit by the predeterminedtransform processing, and an inverse transform unit configured togenerate a composite image of the first image and the second image byperforming inverse transform processing of transform processing by themultiresolution analysis unit for an operation result obtained by thefilter operation unit.

FIG. 1 is a block diagram showing the system configuration of anultrasonic diagnosis apparatus 10 according to this embodiment. As shownin FIG. 1, the ultrasonic diagnosis apparatus 10 includes an ultrasonicprobe 12, an input device 13, a monitor 14, a transmission/receptionunit 21, a B-mode processing unit 22, a Doppler processing unit 23, animage generating circuit 24, a control processor 25, an internal storagedevice 26, an interface 29, a storage unit 30, and a position sensorunit 31.

Obviously, the transmission/reception unit 21 and the like may beimplemented by hardware such as integrated circuits or may beimplemented by software modules as software programs.

The ultrasonic probe 12 is a device (probe) in charge of transmittingand receiving ultrasonic signals applied/reflected to/from an object Pbased on driving signals from the transmission/reception unit 21, and isformed by a piezoelectric element such as a piezoelectric ceramicelement serving as an electromechanical reversible transducer element.The ultrasonic probe 12 is of, for example, a phased array type thathas, on its distal end portion, a plurality of piezoelectric elementsarranged in an array. With this arrangement, the ultrasonic probe 12converts an applied pulse driving voltage into an ultrasonic pulsesignal, transmits it in a desired direction in a scan area of an object,and converts an ultrasonic signal reflected by the object into an echosignal with a voltage corresponding to the ultrasonic signal.

Assume that the ultrasonic probe 12 is, for example, a two-dimensionalarray probe (a probe having ultrasonic transducers arranged in a matrixform) capable of three-dimensionally scanning an object. Obviously,however, the ultrasonic probe 12 may be a one-dimensional array probe (aprobe having ultrasonic transducers arranged along one direction)configured to perform three-dimensional scanning by a manual,mechanical, or divergent beam system.

The input device 13 is connected to the ultrasonic diagnosis apparatus10 and includes a trackball 13 a and various switches 13 b forinputting, to the ultrasonic diagnosis apparatus 10, variousinstructions from the operator, an instruction to set a region ofinterest (ROI), and various instructions to set various image qualityconditions.

The monitor 14 displays images of morphological information, blood flowinformation, and the like inside a living body based on video signalsgenerated by the image generating circuit 24.

The transmission/reception unit 21 includes a pulser circuit, delaycircuit, trigger generating circuit, amplifier circuit, A/D converter,and adder all of which are not shown. The pulser circuit repetitivelygenerates rate pulses for the formation of transmission ultrasonic wavesat a predetermined rate frequency fr Hz (period: 1/fr sec). The delaycircuit gives each rate pulse a delay time necessary to focus anultrasonic wave into a beam and determine transmission directivity foreach channel. The trigger generating circuit applies a driving pulse tothe ultrasonic probe 12 at the timing based on this rate pulse. Theamplifier circuit amplifies an echo signal received through theultrasonic probe 12 for each channel. The A/D converter gives eachamplified echo signal a delay time necessary to determine receptiondirectivity. The adder then performs addition processing to enhance thereflection component of the echo signal from the direction correspondingto the reception directivity, and forms a synthetic beam for ultrasonictransmission/reception in accordance with the reception directivity andtransmission directivity.

Note that the transmission/reception unit 21 has a function of instantlychanging a transmission frequency, transmission driving voltage, or thelike to execute a predetermined scan sequence in accordance with aninstruction from the control processor 25.

The B-mode processing unit 22 performs logarithmic amplification andenvelope detection processing for the echo signal output from thetransmission/reception unit 21 to generate data whose signal intensityis expressed by a luminance level. This data is output to the imagegenerating circuit 24. The monitor 14 then displays the data as a B-modeimage representing the intensity of a reflected wave by luminance.

The Doppler processing unit 23 frequency-analyzes velocity informationand outputs, to the image generating circuit 24, the analysis result asa signal carrying blood flow or tissue moving velocity informationinside the object.

The image generating circuit 24 performs coordinate conversion of outputsignals from the B-mode processing unit 22 and the Doppler processingunit 23 into display coordinates, and performs various kinds of imageprocessing and composition processing associated with a B-mode image andCFM (Color Flow Mapping) image. The image generating circuit 24 furtherperforms various kinds of quantitative analyses and measurements basedon these images, and performs image processing such as addinginformation indicating the respective results to the images. The imagegenerating circuit 24 then converts the resultant image signals intoscan signals for TV and outputs them as video signals to the monitor 14.

Note that when performing multiplane display based on three-dimensionalscanning (displaying a plurality of ultrasonic images associated withdifferent slices), the image generating circuit 24 generates image datafor sequentially or simultaneously displaying images of a plurality ofslices obtained by three-dimensional scanning in a predetermined form.

The control processor 25 is a control unit which has a function as aninformation processing apparatus (computer) and comprehensively controlsthe operation of the ultrasonic diagnosis apparatus 10. The controlprocessor 25 reads out control programs for the execution of imagegenerating/display operation from the internal storage device 26 asneeded. The control processor 25 expands the control program in a memoryarea which it has, and executes arithmetic operation/control operationand the like associated with various kinds of processing.

The internal storage device 26 stores control programs for the executionof a predetermined scan sequence, image generation, and displayprocessing, diagnosis information (a patient ID, findings by a doctor,and the like), a diagnosis protocol, transmission/reception conditions,and other data. Note it is possible to transfer data stored in theinternal storage device 26 to an external peripheral apparatus via theinterface 29.

The interface 29 is an interface for connection to the input device 13,a network, an external storage device (not shown), and the like. Notethat the interface 29 can transfer data such as ultrasonic images,analysis results, and the like acquired by the ultrasonic diagnosisapparatus 10 to another apparatus via a network.

The storage unit 30 includes an image memory 30 a to store the imagesacquired by the ultrasonic diagnosis apparatus 10 and various kinds ofimages acquired via a network and a software storage unit 30 b to storesoftware for causing the ultrasonic diagnosis apparatus 10 to function.

The position sensor unit 31 is a sensor unit for detecting the positionand direction of the ultrasonic probe 12.

This embodiment is configured to perform composition processing ofimages acquired by scanning using different kinds of transmission waves.FIG. 2 is a view showing the principle of image composition processingby the image generating circuit 24 in the ultrasonic diagnosis apparatus10 according to the embodiment. The above image composition processingwill be described by taking, as an example, a case in which two images,namely images a and b, are composited.

First of all, the image generating circuit 24 uses an ultrasonic wavehaving a transmission waveform with a relatively long wave train lengthas a transmission wave, and extracts only signal components in thesecond harmonic band from a reception wave through a bandpass filter,thereby acquiring the image a which is a sensitivity-priority image.Note that it is possible to receive second harmonic components with highsensitivity by using a phase modulation technique (called phaseinversion, phase modulation, phase subtraction, and the like). The imagegenerating circuit 24 then uses an ultrasonic wave having a transmissionwaveform with a relatively short wave train length as a transmissionwave, and visualizes the second harmonic band like the image a, therebyobtaining the image b which is a resolution-priority image.

The image generating circuit 24 uses a transmission sequence ofswitching between transmission of a transmission wave for theacquisition of the image a and transmission of a transmission wave forthe acquisition of the image b frame by frame. Obviously, it is possibleto switch transmission and reception raster by raster.

The image generating circuit 24 performs coordinate conversion of theimages acquired by the respective types of scanning and then performsmultiresolution analysis of the two consecutive images. In addition, toperform multiresolution analysis, two-dimensional discrete wavelettransform at an L level (for example, about 2 or 3) is performed. Thisoperation will be described in detail below.

The following is a case in which two-dimensional discrete wavelettransform at level 3 is performed to perform multiresolution analysis ofthe images a and b.

First of all, the image generating circuit 24 performs two-dimensionaldiscrete wavelet transform (dwt) at level 1 to decompose each of theimages a and b into wavelet coefficients A1 (Approximation), H1(Horizontal detail), V1 (Vertical detail), and D1 (Diagonal detail) (therespective wavelet coefficients of the image a will be referred to as A1a, H1 a, V1 a, and D1 a, and the respective wavelet coefficients of theimage b will be referred to as A1 b, H1 b, V1 b, and D1 b; that is, ∘∘aand ∘∘b respectively represent coefficients ∘∘ of the images a and b).

The image generating circuit 24 then obtains A2, H2, V2, and D2 byfurther performing two-dimensional discrete wavelet transform of A1. Theimage generating circuit 24 obtains A3, H3, V3, and D3 by furtherperforming two-dimensional discrete wavelet transform of A2. The aboveprocessing is performed for both the images a and b.

In this case, new coefficients are generated by performing predeterminedarithmetic operations using the corresponding wavelet coefficients. Asthe predetermined arithmetic operations, for example, the followingarithmetic operations can be presented:

setting A3=A3 a, or executing Mean(A3 a, A3 b), or increasing the ratioof A3 a like 0.8*A3 a+0.2*A3 b, and adding/averaging the correspondingwavelet coefficients;

setting H3=H3 b or increasing the ratio of H3 b, and adding/averagingthe corresponding wavelet coefficients;

setting V3=V3 b or increasing the ratio of V3 b, and adding/averagingthe corresponding wavelet coefficients;

setting D3=D3 b or increasing the ratio of D3 b, and adding/averagingthe corresponding wavelet coefficients;

setting H2=H2 b or increasing the ratio of H2 b, and adding/averagingthe corresponding wavelet coefficients;

setting V2=V2 b or increasing the ratio of V2 b, and adding/averagingthe corresponding wavelet coefficients;

setting D2=D2 b or increasing the ratio of D2 b, and adding/averagingthe corresponding wavelet coefficients;

setting H1=H1 b or increasing the ratio of H1 b, and adding/averagingthe corresponding wavelet coefficients;

setting V1=V1 b or increasing the ratio of V1 b, and adding/averagingthe corresponding wavelet coefficients; and

setting D1=D1 b or increasing the ratio of D1 b, and adding/averagingthe corresponding wavelet coefficients.

In this case, Mean(a, b) represents the arithmetic operation ofcalculating the average value of coefficients ∘∘ of the images a and b.Note that if b=0, it is possible to output the value of a, whereas ifa=0, it is possible to output the value of b.

Upon performing the above arithmetic operations, the image generatingcircuit 24 calculates the coefficient A2 by performing two-dimensionalinverse discrete wavelet transform (idwt) for the coefficients A3, H3,V3, and D3. The image generating circuit 24 further calculates thecoefficient A1 by performing two-dimensional inverse discrete wavelettransform for the coefficients A2, H2, V2, and D2. The image generatingcircuit 24 then obtains a composite image from the coefficients A1, H1,V1, and D1 by performing two-dimensional inverse discrete wavelettransform.

The above image composition processing will be described in detailbelow. This processing will be described by taking, as an example, acase in which arithmetic operations are performed for two frames foreach wavelet coefficient.

First of all, assume that the arithmetic function to be used is func(a1,a2, . . . , aN, M, Level), where a1, a2, . . . , aN represent inputdata, M=0 represents Approximation, M=1 represents Horizontal detail,M=2 represents Vertical detail, M=3 represents Diagonal detail, andLevel represents a wavelet expansion count. In addition idwt2 representstwo-dimensional inverse discrete wavelet transform.

The above arithmetic operations are represented by

A(L)=func(A(L,n),A(L,n−1), . . . ,A(L,n−k),0,L)

H(L)=func(H(L,n),H(L,n−1), . . . ,H(L,n−k),1,L)

V(L)=func(V(L,n),V(L,n−1), . . . ,V(L,n−k),2,L)

D(L)=func(D(L,n),D(L,n−1), . . . ,D(L,n−k),3,L)

H(L−1)=func(H(L−1,n),H(L−1,n−1), . . . ,H(L−1,n−k),1,L−1)

V(L−1)=func(V(L−1,n),V(L−1,n−1), . . . ,V(L−1,n−k),2,L−1)

D(L−1)=func(D(L−1,n),D(L−1,n−1), . . . ,D(L−1,n−k),3,L−1)

. . .

H(1)=func(H(L,n),H(L,n−1), . . . ,H(L,n−k),1,1)

V(1)=func(V(L,n),V(L,n−1), . . . ,V(L,n−k),2,1)

D(1)=func(D(L,n),D(L,n−1), . . . ,D(L,n−k),3,1)

A(L−1)=idwt2(A(L),H(L),V(L),D(L))

A(L−2)=idwt2(A(L−1),H(L−1),V(L−1),D(L−1))

. . .

A(0)=idwt2(A(1),H(1),V(1),D(1))

In this case, A(0) represents an image after composition processing.

The following is an example of the arithmetic functions. That is, at allLevels,

func(a1, a2, . . . , aN, M, Level)=a M=0

func(a1, a2, . . . , aN, M, Level)=a2 M=1, 2, 3

That is, with regard to Approximation, arithmetic operations areperformed by using the coefficients of the image a. With regard to othercoefficients, arithmetic operations are performed by using thecoefficients of the image b. Obviously, it is possible to executeadding/averaging processing with predetermined allocation ratios insteadof using one of the sets of the coefficients of the images a and b. Itis also possible to set allocation ratios in such adding/averagingprocessing to the values desired by the user. It is obviously possibleto provide an operation unit or the like to allow the user to performsuch setting.

As described above, using the coefficients of the image a with regard to“Approximation” coefficient can obtain a rough luminance distributionand structure of an image from the image a. On the other hand, since theinformation of the image b is reflected in “detail” coefficients of H,V, D, the resultant image will inherit the apparent granularity and thelike of the image b.

In this case, the following merit is obtained when acquiring the imagesa and b by switching the above transmission waves frame by frame andgenerating a composite image c by performing the above image compositionprocessing between two consecutive frames, as shown in, for example,FIG. 3. When the monitor 14 displays the composite image generated bythe above processing, the apparent frame rate does not change.

On the other hand, assume that the images a and b are acquired byswitching transmission waves alternately for each raster, and thecomposite image c is generated by performing the above compositionprocessing for each raster, as shown in, for example, FIG. 4. A merit ofthis processing is that when the monitor 14 displays the generatedcomposite image, the time phase shift in a frame is small. In this case,however, the frame rate decreases.

To overcome this problem, it is possible to suppress a decrease in framerate by changing the number of rasters for the images a and b (reducingthe number of transmission wave rasters of the image a to almost half).Since high resolution is not required for the image a, reducing thenumber of transmission wave raters to about half will pose no problem.

In the above case, although arithmetic operations are performed by usingonly the coefficient of each pixel, it is obviously possible to performarithmetic operations by using the coefficients of neighboring pixels.For example, as a method of calculating the “AbsMax” of two images atposition (x, y), it is possible to use a method of calculating anaverage value a1 of neighboring 5×5 points including (x, y) of the firstimage, calculating an average value a2 of neighboring 5×5 pointsincluding (x, y) of the second image, and using a larger one of theabsolute values of a1 and a2 as an output value at (x, y).

The above sequence of image composition processing performed by theultrasonic diagnosis apparatus according to this embodiment can beeasily on sale and distributed as a single software product independentof the ultrasonic diagnosis apparatus by programming the sequence orstoring the resultant program in a storage medium. In addition, thetechnique according to the embodiment can be used on other hardware.

As described above, this embodiment can provide an ultrasonic diagnosisapparatus and program which can acquire a video satisfying both therequirements for sensitivity (luminance) and resolution (visibility).

More specifically, it is possible to generate an image inheriting boththe merits of the images a and b by performing arithmetic operations ofmainly outputting the coefficients of the image a with high sensitivity(high luminance) with regard to low-frequency components acquired bywavelet transform and mainly outputting the coefficients of the image bwith high resolution (high visibility) with regard to high-frequencycomponents. Therefore, the merits of two images having differentadvantages can be expressed on one image. This contributes to animprovement in examination efficiency and diagnosis efficiency.

Obviously, it is also possible to perform filter processing for edgeenhancement and speckle reduction in combination with the above imagecomposition processing by the ultrasonic diagnosis apparatus accordingto the above embodiment. This processing will be described in detailbelow.

[First Modification]

This apparatus may perform processing as a combination ofmultiresolution analysis like that disclosed in, for example, Jpn. Pat.Appln. KOKAI Publication No. 2006-116307 and a nonlinear diffusionfilter like that described below before the above image compositionprocessing. Performing such processing can generate a more viewableimage with less speckle and enhanced edges.

A speckle removal function using a nonlinear diffusion filter will bedescribed in detail below. This speckle removal function acquireslow-frequency decomposed image data at the first to nth levels (where nis a natural number equal to or more than two) and high-frequencydecomposed image data at the first to nth levels by hierarchicallyperforming multiresolution decomposition of image data (raw data) beforeso-called scan conversion processing by the image generating circuit 24.The function then performs nonlinear anisotropic diffusion filtering foroutput data from the next lower layer or the low-frequency decomposedimage data on the lowest layer, and filtering for generating edgeinformation of a signal for each layer from the output data from thenext lower layer or the low-frequency decomposed image data on thelowest layer.

This function also controls the signal level of the high-frequencydecomposed image data for each layer based on the edge information oneach layer and hierarchically performs multiresolution composition ofthe output data from the nonlinear anisotropic diffusion filter and theoutput data of the high-frequency level control, which are obtained oneach layer. Performing such processing will remove speckle by thesynergetic effect of the multiresolution decomposition and the nonlinearanisotropic diffusion filter processing. For the sake of a concretedescription, this embodiment will exemplify a case in which the number nof levels of multiresolution decomposition is 3. However, this case ismerely an example, and the number n may be any value as long as it is anatural number equal to or more than two, for example.

FIG. 5 is a block diagram showing an example of the system configurationof an ultrasonic diagnosis apparatus according to this modification. Themain difference between the ultrasonic diagnosis apparatus according tothis modification and the ultrasonic diagnosis apparatus according tothe above embodiment is whether they include a speckle removalprocessing unit 46.

FIG. 6 is a view showing processing (speckle removal processing)procedure executed by the speckle removal processing unit 46.

As shown in FIG. 6, first of all, a wavelet transform unit 261 a oflevel 1 performs multiresolution decomposition of image data (raw data)input from the B-mode processing unit 22. In this case, “wavelettransform” means discrete wavelet transform. In addition, wavelettransform is an example of multiresolution decomposition, and thetechnical idea of the present invention is not limited to thistechnique. For example, multiresolution decomposition may be implementedby other techniques such as the Laplacian pyramid method.

As a result of the multiresolution decomposition, image data afterdecomposition is decomposed into a low-frequency image LL, a horizontalhigh-frequency image LH, a vertical high-frequency image HL, and adiagonal high-frequency image HH, of which horizontal and verticallengths are half of those before the decomposition. Among the image dataacquired by the decomposition, the wavelet transform unit 261 a outputsthe low-frequency image LL to a wavelet transform unit 262 a of level 2and outputs the horizontal high-frequency image LH, the verticalhigh-frequency image HL, and the diagonal high-frequency image HH to ahigh-frequency level control unit 261 b.

The wavelet transform unit 262 a of level 2 acquires the low-frequencyimage LL, the horizontal high-frequency image LH, the verticalhigh-frequency image HL, and the diagonal high-frequency image HH byperforming multiresolution decomposition of the low-frequency image LLoutput from the wavelet transform unit 261 a of level 1. The wavelettransform unit 262 a outputs the low-frequency image LL to a wavelettransform unit 263 a of level 2 and outputs the horizontalhigh-frequency image LH, the vertical high-frequency image HL, and thediagonal high-frequency image HH to a high-frequency level control unit262 b.

The wavelet transform unit 263 a of level 2 acquires the low-frequencyimage LL, the horizontal high-frequency image LH, the verticalhigh-frequency image HL, and the diagonal high-frequency image HH byperforming multiresolution decomposition of the low-frequency image LLoutput from the wavelet transform unit 262 a of level 2. The wavelettransform unit 263 a outputs the low-frequency image LL to a nonlinearanisotropic diffusion filter 263 c of level 3, and outputs thehorizontal high-frequency image LH, the vertical high-frequency imageHL, and the diagonal high-frequency image HH to a high-frequency levelcontrol unit 263 b.

The nonlinear anisotropic diffusion filter 263 c of level 3 thenperforms filtering of the low-frequency image LL and outputs thelow-frequency image LL after the filtering to an inverse wavelettransform unit 263 d. In addition, the nonlinear anisotropic diffusionfilter 263 c of level 3 generates edge information based on thelow-frequency image LL and outputs the information to the inversewavelet transform unit 263 d.

A nonlinear anisotropic diffusion filter will be described below. Thenonlinear anisotropic diffusion filter is expressed by the followingpartial differential equation (1).

$\begin{matrix}{\frac{\partial I}{\partial t} = {{div}\left\lbrack {D{\nabla I}} \right\rbrack}} & (1)\end{matrix}$where I is the pixel level of an image to be processed, ∇I is thegradient vector of the image, t is the time for the processing, and D isdiffusion tensor, which can be expressed by the following expression(2).

$\begin{matrix}{D = {\begin{pmatrix}d_{11} & d_{12} \\d_{12} & d_{22}\end{pmatrix} = {{R\begin{pmatrix}\lambda_{1} & 0 \\0 & \lambda_{2}\end{pmatrix}}R^{T}}}} & (2)\end{matrix}$where R is a rotation matrix. The diffusion tensor D indicates anarithmetic operation of applying coefficients λ1 and λ2 to the gradientvector of each pixel in a specific direction and a directionperpendicular to the specific direction. The direction is the directionof the edge of a detected image, and the coefficient depends on the sizeof the edge.

In order to detect the size and direction of the edge, it is general toacquire the structure tensor of the image and calculate its eigenvalueand eigenvector. The eigenvalue is associated with the size of the edge,and the eigenvector indicates the direction of the edge. The structuretensor is defined by equation (3) given below.

$\begin{matrix}{S = {{G_{\rho}*\begin{pmatrix}I_{x}^{2} & {I_{x}I_{y}} \\{I_{x}I_{y}} & I_{y}^{2}\end{pmatrix}} = {\begin{pmatrix}{G_{\rho}*I_{x}^{2}} & {G_{\rho}*\left( {I_{x}I_{y}} \right)} \\{G_{\rho}*\left( {I_{x}I_{y}} \right)} & {G_{\rho}*I_{y}^{2}}\end{pmatrix} = \begin{pmatrix}s_{11} & s_{12} \\s_{12} & s_{22}\end{pmatrix}}}} & (3)\end{matrix}$where Ix and Iy represent spatial differentiation of the image I to beprocessed in the x (horizontal) and y (vertical) directions, Gprepresents a two-dimensional Gaussian function, and an operator “*”represents convolution. The calculation of the size and direction of anedge need not strictly follow the above method. Instead of calculatingIx and Iy as the first step in processing, a sobel filter or ahigh-frequency component of multiresolution decomposition may beapplied.

Although the method of calculating the coefficients λ1 and λ2 differsdepending on the characteristics of an ultrasonic image in eachdiagnostic field, it is useful to prepare a general expression so thatthe coefficients can be adjusted by some parameters.

In addition, the calculation of the filter itself is performed by anumerical analysis method using a partial differential equation. Thatis, from the pixel level of a pixel at a given point and pixel levelsof, for example, pixels at nine points around the pixel and each elementvalue of diffusion tensor at time t, a new pixel level at the point attime t+Δt is calculated. The same calculation is then repeated once toseveral times with t+Δt as new t.

FIG. 7 is a flowchart showing a filter processing procedure executed bythe nonlinear anisotropic diffusion filter 263 c (or 261 c or 262 c). Asshown in FIG. 7, the nonlinear anisotropic diffusion filter 263 cdifferentiates the input low-frequency image LL in the x and ydirections (step S1) and calculates structure tensors s11, s12, and s22(step S2). Note that the calculation in step S2 also includescalculation using a Gaussian filter.

The nonlinear anisotropic diffusion filter 263 c calculates the size ofthe edge from each element of the structure tensor (step S3). Thiscalculation result is used for partial differential equation calculationin a subsequent stage and processing in the high-frequency level controlunit 263 b (or 262 b or 261 b).

The nonlinear anisotropic diffusion filter 263 c then calculates eachcoefficient used in the numerical analysis of the partial differentialequation of the nonlinear anisotropic diffusion filter based on eachelement of the structure tensor (step S4). In addition, the processingin this step also includes the calculation of the structure tensor, andalso uses the size of the edge in the calculation for efficientprocessing.

The nonlinear anisotropic diffusion filter 263 c repeatedly executesnumerical-analytical calculation of the partial differential equationonce or several times (step S5). The result obtained by the calculationis output to the inverse wavelet transform unit 263 d (or 261 d or 262d).

As shown in FIG. 6, the high-frequency level control unit 263 b of level3 receives the horizontal high-frequency image LH, the verticalhigh-frequency image HL, the diagonal high-frequency image HH, and edgeinformation associated with these three components and controls ahigh-frequency level according to the images and the edge information.In this embodiment, the edge information is the size of an edgenormalized based on the eigenvalue of the structure tensor, a product ofthe size and each high-frequency image is calculated for each pixel, anda control coefficient of each high-frequency image is applied to theresult.

As another example, there is a method of setting a threshold for thesize of an edge, determining that a given portion is an edge when itssize is equal to or more than the threshold, and applying a controlcoefficient of each high-frequency image to a region other than theedge. Three high-frequency images processed in this manner are input tothe inverse wavelet transform unit 263 d.

The inverse wavelet transform unit 263 d forms one composite image fromthe low-frequency image LL output from the nonlinear anisotropicdiffusion filter 263 c and the horizontal high-frequency image LH, thevertical high-frequency image HL, and the diagonal high-frequency imageHH output from the high-frequency level control unit 263 b. Thehorizontal and vertical lengths of the composite image are twice thoseof an input image.

The composite image output from the inverse wavelet transform unit 263 dof level 3 is input to the nonlinear anisotropic diffusion filter 262 cof level 2, is subjected to the same filtering processing as that atlevel 3, and is then transmitted to the low-frequency image input of theinverse wavelet transform unit 262 d.

On the other hand, the high-frequency level control unit 262 b performsthe same high-frequency control as that at level 3 for the horizontalhigh-frequency image LH, the vertical high-frequency image HL, and thediagonal high-frequency image HH output from the wavelet transform unit262 a. The high-frequency level control unit 262 b then transmits theresultant images to the high-frequency image input of the inversewavelet transform unit 262 d. The inverse wavelet transform unit 262 dforms one composite image data from one low-frequency image and threehigh-frequency images in the same manner as level 3.

In addition, the composite image output from the inverse wavelettransform unit 262 d of level 2 is input to the nonlinear anisotropicdiffusion filter 261 of level 1, is subjected to the same filteringprocessing as that at levels 2 and 3, and is then transmitted to thelow-frequency image input of the inverse wavelet transform unit 261 d.

On the other hand, the high-frequency level control unit 261 b performsthe same high-frequency control as that at levels 2 and 3 for thehorizontal high-frequency image LH, the vertical high-frequency imageHL, and the diagonal high-frequency image HH output from the wavelettransform unit 261 a. The high-frequency level control unit 261 b thentransmits the resultant images to the high-frequency image input of theinverse wavelet transform unit 261 d. The inverse wavelet transform unit261 d forms one composite image data from one low-frequency image andthree high-frequency images in the same manner as levels 2 and 3.

The composite image data formed by the above processing is transmittedfrom the speckle removal processing unit 46 to the image generatingcircuit 24. The image generating circuit 24 composites the compositeimage data with character information, scale, and the like of variousparameters, converts the result into a scanning line signal string in ageneral video format typified by a TV format, and generates anultrasonic diagnostic image as a display image. The monitor 14 displaysthe generated ultrasonic image in a predetermined form.

As described above, this modification can provide an ultrasonicdiagnosis apparatus and program which have the following effects inaddition to effects similar to those of the above embodiment.

This ultrasonic diagnosis apparatus hierarchically performsmultiresolution decomposition of image data (raw data) before scanconversion processing to acquire low-frequency decomposed image data atthe first to nth levels (where n is a natural number equal to or morethan two) and high-frequency decomposed image data at the first to nthlevels, performs nonlinear anisotropic diffusion filtering for outputdata from the next lower layer or the low-frequency decomposed imagedata on the lowest layer, and performs filtering for generating edgeinformation of a signal for each layer from the output data from thenext lower layer or the low-frequency decomposed image data on thelowest layer.

In addition, this apparatus controls the signal level of thehigh-frequency decomposed image data for each layer based on the edgeinformation on each layer and hierarchically performs multiresolutioncomposition of the output data from the nonlinear anisotropic diffusionfilter and the output data of the high-frequency level control, whichare obtained on each layer. With this processing, the apparatus removesspeckle by the synergetic effect of the multiresolution decompositionand the nonlinear anisotropic diffusion filter processing. Therefore,compared with a case in which only a filter is applied, speckle removalprocessing in which the speckle is fine and an interface of tissues isclearer can be implemented. As a result, a high-quality diagnostic imagecan be provided, which can contribute to an improvement in the qualityof image diagnosis.

In addition, this ultrasonic diagnosis apparatus applies the nonlinearanisotropic diffusion filter after reducing an image by multiresolutiondecomposition. Accordingly, compared with a case in which a nonlinearanisotropic diffusion filter is applied directly to an original image,the processing area (the amount of data to be processed) can be reduced.As a result, high-speed processing can be implemented compared with anonlinear anisotropic diffusion filter which requires much time forcalculation.

Furthermore, according to the ultrasonic diagnosis apparatus, since onlya B-mode image is processed in speckle removal processing, theprocessing does not influence a color Doppler image even if the colorDoppler image is superimposed on the B-mode image. As a result, it ispossible to implement high-quality speckle removal without restrictingthe degree of freedom in image processing or image display and withoutinfluencing the processing speed even if the resolution of a displaysystem increases.

Note that the image a with poor resolution has a problem associated withfalse recognition of an edge, and hence the above filter processingcannot be sometimes be applied strongly. Therefore, when edgeinformation is to be extracted, the information may be calculated fromthe image b before arithmetic operation between the images, and asmoothing filter may be applied to the image after the arithmeticoperation by using the calculation result. This can suppress falserecognition of an edge and the like.

The above embodiment and modification use discrete wavelet transform asmultiresolution analysis. In general discrete wavelet transform, an LPF(Low Pass Filter) and HPF (High Pass Filter) are applied to information,and the respective outputs are downsampled to half. For this reason, theamount of information does not change before and after the wavelettransform. When inverse wavelet transform is performed after operationof coefficients in wavelet shrinkage, block-like artifact may appear onan image.

In consideration of such a situation, it is conceivable to usestationary wavelet transform as multiresolution analysis instead ofdiscrete wavelet transform.

Stationary wavelet transform is configured not to perform downsampling.For this reason, when, for example, stationary wavelet transform of atwo-dimensional image is performed once, the amount of informationincreases four times. However, even when inverse transform of the imageis performed after wavelet shrinkage, any block-like artifact like thatdescribed above does not appear.

It is also possible to use one of various types of pyramid transformssuch as Laplacian pyramid transform, RoL (Ratio of low pass) pyramidtransform, and gradient pyramid transform as multiresolution analysisinstead of wavelet transform.

The main difference between wavelet transform and pyramid transform isthat wavelet transform is orthogonal transform, but pyramid transform isnot necessarily orthogonal transform. However, like wavelet transform,pyramid transform allows multiresolution analysis.

Note that it is possible to apply the above embodiment and modificationto image composition processing based on the above frequency compound.When, for example, an amplitude modulation method is to be used, sinceonly bubbles are visualized, only the fundamental wave band can be used.In this case, however, the frequency band is narrow, and hence theobtained image has poor resolution. For this reason, this image is setas the image a, and an image containing up to the second harmonic is setas the image b. The above composition processing is performed for theimages a and b to obtain an image with high sensitivity and goodresolution. This method need not switch transmission waves, and hence isfree from a reduction in frame rate.

The above embodiment includes inventions of various stages, and variousinventions can be extracted by proper combinations of a plurality ofdisclosed constituent elements. Even if, for example, severalconstituent elements are omitted from all the constituent elements inthe embodiment, the problem described in “Description of the RelatedArt” can be solved. If the effects described above can be obtained, thearrangement from which these constituent elements are omitted can beextracted as an invention.

[Second Modification]

Contrast-enhanced ultrasonography requires good spatial resolution andhigh contrast-enhanced bubble detection sensitivity, and also requireshigh separation between contrast-enhanced bubbles and the tissue. Thesecond modification has been made in consideration of this situation.

To avoid a redundant description, the differences between the aboveembodiment and the first modification will be described below. Theultrasonic diagnosis apparatus and program according to the secondmodification acquire the images a and b described above by the followingprocessing.

The image a is acquired by a technique known by those skilled in the artas an AM (Amplitude Modulation) method. In the AM method, whenrepetitively generating rate pulses, the pulser circuit repetitivelygenerates the rate pulses while amplitude-modulating them at apredetermined (e.g., 5-kHz) rate frequency fr Hz (period: 1/fr sec).

The AM method provides superior separation between contrast-enhancedbubbles and the tissue, and exhibits high depth sensitivity.

The image b is acquired by a technique known by those skilled in the artas a PM (Phase Modulation) method. In the PM method, when repetitivelygenerating rate pulses, the pulser circuit repetitively generates therate pulses while phase-modulating them at a predetermined (e.g., 5-kHz)rate frequency fr Hz (period: 1/fr sec).

The PM method provides superior spatial resolution. However, the PMmethod is regarded as a method which does not provide superiorseparation between contrast-enhanced bubbles and the tissue.

Like the above embodiment and the first modification, the secondmodification switches between transmission of a transmission wave forthe acquisition of the image a and transmission of a transmission wavefor the acquisition of the image b alternately for each frame or raster.This modification performs multiresolution analysis of the image aobtained by the AM method and the image b obtained by the PM method, andfurther performs composition processing.

As described above, the second modification can obtain effects similarto those of the ultrasonic diagnosis apparatus and program according tothe above embodiment and provide an ultrasonic diagnosis apparatus andprogram which have the following effects.

The second modification can provide an ultrasonic diagnosis apparatusand program which satisfy both the requirements for sensitivity(luminance) and resolution (visibility) and provide good separationbetween contrast-enhanced bubbles and the tissue.

In order to reduce the time difference between acquired images, scanningfor the acquisition of the image a may be performed with a smallernumber of rasters than that of scanning for the acquisition of the imageb. In addition, it is possible to provide a UI for changing thecomposition ratio between the images a and b.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An ultrasonic diagnosis apparatus which acquirestomographic image data by scanning a predetermined region of an objectwith an ultrasonic wave, the apparatus comprising: an ultrasonic probeconfigured to transmit first transmission waves having differentamplitudes and second transmission waves having different phases foreach of scanning lines, and to receive first echoes caused by the firsttransmission waves and second echoes caused by the second transmissionwaves; an image generation unit configured to generate, without use of aDoppler method, a first image based on the first echoes and a secondimage based on the second echoes, wherein a spatial resolution of thefirst image is higher than a spatial resolution of the second image anda sensitivity of the second image is higher than a sensitivity of thefirst image; a multiresolution analysis unit configured to performmultiresolution analysis based on predetermined transform processing onthe first image and the second image; a filter operation unit configuredto perform a predetermined filter operation for correspondingcoefficients of the first image and the second image for eachcoefficient of each spatial resolution acquired by the multiresolutionanalysis unit by the predetermined transform processing; and an inversetransform unit configured to generate a composite image of the firstimage and the second image by performing inverse transform processing ofthe transform processing by the multiresolution analysis unit for anoperation result obtained by the filter operation unit.
 2. The apparatusaccording to claim 1, wherein the multiresolution analysis unitperforms, as the predetermined transform processing, any one of wavelettransform, stationary wavelet transform, and pyramid transform.
 3. Theapparatus according to claim 1, wherein the predetermined filteroperation performed by the filter operation unit comprises one of addingor averaging processing of corresponding coefficients of the first imageand the second image for each spatial resolution and adding or averagingprocessing of coefficients with resolutions lower than a predeterminedspatial resolution upon increasing a ratio of the first image and addingor averaging processing of coefficients with resolutions higher than apredetermined spatial resolution upon increasing a ratio of the secondimage.
 4. The apparatus according to claim 3, wherein the predeterminedfilter operation performed by the filter operation unit comprises afilter operation using coefficients of the first image as thecoefficients of spatial resolutions lower than the predeterminedresolution and using coefficients of the second image as thecoefficients with spatial resolutions higher than the predeterminedspatial resolution.
 5. The apparatus according to claim 1, wherein thefirst image and the second image comprise images obtained by scanningwhile switching the first transmission wave and the second transmissionwave frame by frame.
 6. The apparatus according to claim 1, wherein thefirst image and the second image comprise images obtained by scanningwhile switching the first transmission wave and the second transmissionwave raster by raster.
 7. The apparatus according to claim 1, furthercomprising an edge processing unit configured to extract edge positioninformation from the second image after processing by themultiresolution analysis unit and before processing by the filteroperation unit and perform edge enhancement processing or edgepreserving smoothing filter processing for each coefficient of the firstimage based on the extracted edge information.
 8. The apparatusaccording to claim 1, wherein a wave train length of the firsttransmission wave is longer than a wave train length of the secondtransmission wave.
 9. The apparatus according to claim 1, wherein thesensitivity indicates a detectability of injected contrast mediumbubbles.
 10. The apparatus according to claim 1, wherein the number ofthe first transmission waves transmitted by the ultrasonic probe is ½the number of the second transmission waves generated transmitted by theultrasonic probe.
 11. An ultrasonic diagnosis apparatus which acquirestomographic image data by scanning a predetermined region of an object,in which region a contrast medium has been injected, with an ultrasonicwave, the apparatus comprising: an ultrasonic probe configured totransmit first transmission waves having different amplitudes and secondtransmission waves having different phases for each of scanning lines,and to receive first echoes caused by the first transmission waves andsecond echoes caused by the second transmission waves; an imagegeneration unit configured to generate, without use of a Doppler method,a first image based on the first echoes and a second image based on thesecond echoes, wherein a spatial resolution of the first image is higherthan a spatial resolution of the second image and a sensitivity of thesecond image is higher than a sensitivity of the first image; amultiresolution analysis unit configured to perform multiresolutionanalysis based on predetermined transform processing on a first imageand a second image; a filter operation unit configured to performpredetermined filter operation for corresponding coefficients of thefirst image and the second image for each coefficient of each spatialresolution acquired by the multiresolution analysis unit by thepredetermined transform processing; and an inverse transform unitconfigured to generate a composite image of the first image and thesecond image by performing inverse transform processing of transformprocessing by the multiresolution analysis unit for an operation resultobtained by the filter operation unit.
 12. A non-transitory computerreadable medium including computer executable instructions which cause acomputer to function as an ultrasonic diagnosis apparatus which acquirestomographic image data by scanning a predetermined region of an objectwith an ultrasonic wave, the program causing the computer to implement:a function of transmitting first transmission waves having differentamplitudes and second transmission waves having different phases foreach of scanning lines, and receiving first echoes caused by the firsttransmission waves and second echoes caused by the second transmissionwaves, an image generation function to generate, without use of aDoppler method, a first image based on the first echoes and a secondimage based on the second echoes, wherein a spatial resolution of thefirst image is higher than a spatial resolution of the second image anda sensitivity of the second image is higher than a sensitivity of thefirst image; a multiresolution analysis function of performingmultiresolution analysis based on predetermined transform processing onthe first image and the second image, a filter operation function ofperforming predetermined filter operation for corresponding coefficientsof the first image and the second image for each coefficient of eachspatial resolution acquired by the multiresolution analysis function bythe predetermined transform processing, and an inverse transformfunction of generating a composite image of the first image and thesecond image by performing inverse transform processing of transformprocessing by the multiresolution analysis function for an operationresult obtained by the filter operation function.
 13. A non-transitorycomputer readable medium including computer executable instructionswhich cause a computer to function as an ultrasonic diagnosis apparatuswhich acquires tomographic image data by scanning a predetermined regionof an object, in which a contrast medium has been injected, with anultrasonic wave, the program causing the computer to implement: afunction of transmitting first transmission waves having differentamplitudes and second transmission waves having different phases foreach of scanning lines, and receiving first echoes caused by the firsttransmission waves and second echoes caused by the second transmissionwaves, an image generation function to generate, without use of aDoppler method, a first image based on the first echoes and a secondimage based on the second echoes, wherein a spatial resolution of thefirst image is higher than a spatial resolution of the second image anda sensitivity of the second image is higher than a sensitivity of thefirst image, a multiresolution analysis function of performingmultiresolution analysis based on predetermined transform processing ona first image and a second image, a filter operation function ofperforming predetermined filter operation for corresponding coefficientsof the first image and the second image for each coefficient of eachspatial resolution acquired by the multiresolution analysis function bythe predetermined transform processing, and an inverse transformfunction of generating a composite image of the first image and thesecond image by performing inverse transform processing of transformprocessing by the multiresolution analysis function for an operationresult obtained by the filter operation function.
 14. The apparatusaccording to claim 1, wherein the first transmission wave is generatedby an amplitude modulation and the second transmission wave is generatedby a phase modulation.